{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derivatives Analytics with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Chapter 11_**\n",
    "\n",
    "**Wiley Finance (2015)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/images/derivatives_analytics_front.jpg\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yves Hilpisch\n",
    "\n",
    "The Python Quants GmbH\n",
    "\n",
    "<a href='mailto:analytics@pythonquants.com'>analytics@pythonquants.com</a>\n",
    "\n",
    "<a href='http://pythonquants.com'>www.pythonquants.com</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 11: Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short Rate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%run 11_cal/CIR_calibration.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, -0.000130, -0.000130, 0.000070, 0.000430, 0.000830,\n",
       "       0.001830, 0.002510, 0.003380])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_list  # spot rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, -0.000130, -0.000130, 0.000070, 0.000430, 0.000830,\n",
       "       0.001829, 0.002508, 0.003374])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.002778, 0.019444, 0.038889, 0.083333, 0.166667, 0.250000,\n",
       "       0.500000, 0.750000, 1.000000])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.146849, 0.104511, 0.175197])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt = CIR_calibration()\n",
    "opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000387, -0.000063, 0.000585, 0.000794, 0.001210, 0.001754,\n",
       "       0.002155, 0.002463, 0.002730, 0.002960, 0.003155, 0.003317,\n",
       "       0.003454, 0.003594, 0.003751, 0.003938, 0.004168, 0.004452,\n",
       "       0.004804, 0.005234, 0.005757, 0.006383, 0.007125, 0.007996])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ys6+rHHev8yD0eKgHw3sNZ/b/m30A305ERCR81CcHoZVXjdkb51w74AngcDMr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\n26/5C1lp7UkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd91b935d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_term_structure()\n",
    "plt.savefig('../images/11_cal/term_structure.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.000320, 0.000015, 0.000347, 0.000677, 0.001006, 0.001332,\n",
       "       0.001656, 0.001977, 0.002297, 0.002614, 0.002930, 0.003243,\n",
       "       0.003554, 0.003863, 0.004169, 0.004474, 0.004776, 0.005076,\n",
       "       0.005374, 0.005670, 0.005963, 0.006254, 0.006544, 0.006831])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIR_forward_rate(opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vz5g4ZAgTU9a3EZHCl8Q8OQ8A482sK7CRme0CvAy8nPCSDiKS55YvjxKZ119f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\nJ/TSzLynXH32tcA1wOOA99JcYv8c4Eaay+/vU/Y5GvhoZn4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd91b15f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibrated_frc(opt)\n",
    "plt.savefig('../images/11_cal/frc_calibration.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3uvst4f7T3X0NsBvwWcK5PiVYuAgzuyGhfE9gQQqxiIi0qGefhZ49g0TgnXdg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd91b04c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_zcb_values(opt, 2.0)\n",
    "plt.savefig('../images/11_cal/CIR_zcb_values.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Volatility Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    }
   ],
   "source": [
    "%run 11_cal/H93_calibration.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Strike</th>\n",
       "      <th>Call</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>Put</th>\n",
       "      <th>T</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175</td>\n",
       "      <td>126.8</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>78.8</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200</td>\n",
       "      <td>110.9</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>87.9</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225</td>\n",
       "      <td>96.1</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>98.1</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250</td>\n",
       "      <td>82.3</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>109.3</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275</td>\n",
       "      <td>69.6</td>\n",
       "      <td>2014-12-19</td>\n",
       "      <td>121.6</td>\n",
       "      <td>0.219178</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>129.2</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200</td>\n",
       "      <td>156.1</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>139.4</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>150.3</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250</td>\n",
       "      <td>128.5</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>161.8</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275</td>\n",
       "      <td>115.8</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.468493</td>\n",
       "      <td>0.003203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3175</td>\n",
       "      <td>82.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3200</td>\n",
       "      <td>64.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3225</td>\n",
       "      <td>48.3</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3250</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>51.8</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>3275</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Strike   Call   Maturity    Put         T         r\n",
       "38  2014-09-30    3175  126.8 2014-12-19   78.8  0.219178  0.001349\n",
       "39  2014-09-30    3200  110.9 2014-12-19   87.9  0.219178  0.001349\n",
       "40  2014-09-30    3225   96.1 2014-12-19   98.1  0.219178  0.001349\n",
       "41  2014-09-30    3250   82.3 2014-12-19  109.3  0.219178  0.001349\n",
       "42  2014-09-30    3275   69.6 2014-12-19  121.6  0.219178  0.001349\n",
       "342 2014-09-30    3175  171.0 2015-03-20  129.2  0.468493  0.003203\n",
       "343 2014-09-30    3200  156.1 2015-03-20  139.4  0.468493  0.003203\n",
       "344 2014-09-30    3225  142.0 2015-03-20  150.3  0.468493  0.003203\n",
       "345 2014-09-30    3250  128.5 2015-03-20  161.8  0.468493  0.003203\n",
       "346 2014-09-30    3275  115.8 2015-03-20  174.0  0.468493  0.003203\n",
       "456 2014-09-30    3175   82.3 2014-10-17   24.5  0.046575  0.000038\n",
       "457 2014-09-30    3200   64.3 2014-10-17   31.5  0.046575  0.000038\n",
       "458 2014-09-30    3225   48.3 2014-10-17   40.5  0.046575  0.000038\n",
       "459 2014-09-30    3250   34.6 2014-10-17   51.8  0.046575  0.000038\n",
       "460 2014-09-30    3275   23.5 2014-10-17   65.8  0.046575  0.000038"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [2.500000 0.010000 0.050000 -0.750000 0.010000] | 1383.261 | 500.000\n",
      "  25 | [2.500000 0.020000 0.050000 -0.750000 0.010000] | 776.135 |  37.017\n",
      "  50 | [2.500000 0.020000 0.250000 -0.750000 0.020000] | 189.987 |  11.029\n",
      "  75 | [2.500000 0.030000 0.150000 -0.750000 0.030000] |  73.318 |   7.465\n",
      " 100 | [2.500000 0.040000 0.050000 -0.500000 0.010000] | 215.473 |   7.465\n",
      " 125 | [2.500000 0.040000 0.250000 -0.500000 0.020000] |  34.849 |   7.465\n",
      " 150 | [7.500000 0.010000 0.150000 -0.500000 0.030000] | 344.185 |   7.465\n",
      " 175 | [7.500000 0.020000 0.050000 -0.250000 0.010000] | 433.526 |   7.465\n",
      " 200 | [7.500000 0.020000 0.250000 -0.250000 0.020000] | 151.386 |   7.465\n",
      " 225 | [7.500000 0.030000 0.150000 -0.250000 0.030000] |  87.733 |   7.465\n",
      " 250 | [7.500000 0.040000 0.050000 0.000000 0.010000] | 171.852 |   7.465\n",
      " 275 | [7.500000 0.040000 0.250000 0.000000 0.020000] | 227.431 |   7.465\n",
      " 300 | [2.077511 0.022658 0.220399 -0.870570 0.027501] |   5.897 |   5.683\n",
      " 325 | [1.777303 0.023867 0.223357 -0.899849 0.027612] |   5.539 |   5.358\n",
      " 350 | [1.518360 0.023366 0.243363 -0.968085 0.027728] |   5.167 |   5.147\n",
      " 375 | [2.026923 0.023695 0.237268 -0.991909 0.027731] |   5.043 |   4.997\n",
      " 400 | [2.819864 0.023648 0.235749 -0.997912 0.028041] |   4.966 |   4.953\n",
      " 425 | [3.437130 0.023513 0.247484 -0.999161 0.028632] |   4.901 |   4.895\n",
      " 450 | [3.568656 0.023414 0.251514 -0.999727 0.028766] |   4.890 |   4.889\n",
      " 475 | [3.893760 0.023406 0.253386 -0.999988 0.028970] |   4.887 |   4.886\n",
      " 500 | [3.701342 0.023418 0.253344 -0.999794 0.028869] |   4.883 |   4.883\n",
      " 525 | [3.617254 0.023413 0.263216 -0.998451 0.028912] |   4.834 |   4.834\n",
      " 550 | [7.155679 0.024405 0.365684 -0.975347 0.030804] |   3.678 |   3.678\n",
      " 575 | [6.838740 0.026416 0.512457 -0.924022 0.029567] |   2.167 |   1.921\n",
      " 600 | [7.930309 0.027532 0.614542 -0.891392 0.029508] |   1.693 |   1.693\n",
      " 625 | [9.097062 0.026931 0.639631 -0.892909 0.030594] |   1.280 |   1.261\n",
      " 650 | [12.309848 0.026477 0.798270 -0.865478 0.032935] |   0.566 |   0.507\n",
      " 675 | [13.312589 0.026384 0.837364 -0.858920 0.033614] |   0.485 |   0.485\n",
      " 700 | [15.570938 0.025968 0.898136 -0.850395 0.035047] |   0.446 |   0.445\n",
      " 725 | [15.457640 0.026037 0.896562 -0.848604 0.034803] |   0.412 |   0.394\n",
      " 750 | [17.678585 0.025962 0.957309 -0.829704 0.035275] |   0.332 |   0.332\n",
      " 775 | [18.796656 0.025887 0.985318 -0.818473 0.035275] |   0.310 |   0.310\n",
      " 800 | [18.612507 0.025905 0.981962 -0.819479 0.035224] |   0.308 |   0.308\n",
      " 825 | [18.544397 0.025910 0.980290 -0.819801 0.035189] |   0.307 |   0.307\n",
      " 850 | [18.456498 0.025918 0.978113 -0.820421 0.035164] |   0.307 |   0.307\n",
      " 875 | [18.440862 0.025920 0.977737 -0.820548 0.035162] |   0.307 |   0.307\n",
      " 900 | [18.441894 0.025920 0.977758 -0.820556 0.035164] |   0.307 |   0.307\n",
      " 925 | [18.448275 0.025919 0.977921 -0.820509 0.035165] |   0.307 |   0.307\n",
      " 950 | [18.447095 0.025919 0.977892 -0.820517 0.035165] |   0.307 |   0.307\n",
      " 975 | [18.447029 0.025919 0.977890 -0.820518 0.035165] |   0.307 |   0.307\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 1min 50s, sys: 184 ms, total: 1min 50s\n",
      "Wall time: 1min 50s\n"
     ]
    }
   ],
   "source": [
    "%time opt_sv = H93_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.447069, 0.025919, 0.977891, -0.820517, 0.035165])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_sv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_sv)\n",
    "rf = open('11_cal/H93_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options['Model'] = H93_calculate_model_values(opt_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.074370708115957029"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(options['Model'] - options['Call'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4923097895511077"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(abs(options['Model'] - options['Call']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f/ap/du9evwrc0qV+ANzChfDrX/t7vXr5mnnkGDHCb8IiItJc1afP+ytm9v8l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\nIiIiGeb/B1z43S39HxHeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90f03b110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/H93_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options['Model'] = H93_calculate_model_values(opt_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_sv.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    }
   ],
   "source": [
    "%run 11_cal/plot_implied_volatilities.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00139413807758\n",
      "0.0173881320372\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print np.sum(options['model_iv'] - options['market_iv'])\n",
    "# total absolute error\n",
    "print np.sum(abs(options['model_iv'] - options['market_iv']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+5pvut97qftJJ7rvuWn14/8pX3MeMcS8tda+sdN+0qcWnyyr6boaleIajWIYV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90e7f5050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nXTJ8OBNvvFFJjkiCFTrB2Rn4PtHp4XOB77j7om2/Kv/KOcHRMGtYimfTsguf\nsxOh1asbb9+vX4pRo2pyjv60pvBZd3uP6LMZluIZViGnqPJ1vdT93P34PO1bRIpY165bblCaLX3F\n58zk5/nno7O9osLnxtv37p37Vhe5rvisu72LSKZ8jeC8BKSAe919bvADtFE5j+CIlII1a2D+/NxT\nX/PmQWYOk134vKpuIve9pREckXJT6CmqUcB84ERgb6DO3R8OfqBWUoIjUro2b45GeHIlP/X1sO7D\nWRzBWY3u9v7VbsP5aP8b+cwhkxpNf+20k675I5IUhU5w9gLeAo4nuqP4yvRtFDpSOSc4mkcOS/EM\nJ1QsV6yA//vZLF746U1sWrmWjzZ0Z+n2Z7JwxSQWLGhc+NyzZ+MzvjIfQ4ZE02ylSp/NsBTPsJJQ\ng3Mv0YX6HgNOdvc38nQcEREA+vSBU6ZO4pSpW58xtX5946mv9OPtt+HxxyGzTKdz56i+J1fyM3w4\n9OpVuPckIm2XrxGcnwNfd/eVwXfeDuU8giMiuW3eDIsWbX3GV/qRfc2fAQOaTn4GDNDUl0ihFXqK\nqrO7bwq+43ZSgiMirZW+2Wl24lNfDwsWRGeGpfXq1fTUV/ZZXyISRkETnGJVzgmO5pHDUjzDKeVY\nrluXe+orPRqUedZXly5bzvrKfgwbBp/6VJg+lXI8i5HiGVYSanBERBKvWzfYbbfokS37rK/Mx0sv\nwUcfNd5+p52anvrq109TXyKtVZARHDM73N0fy/uBmu9H2Y7giEhx+eij3MlPfT0sXNh424qKppOf\nQYOiwmiRclWQKar4Lt+5DHb34cEO1EZKcESkFKxdG13YMFfyk32n965doaqq6amvHj067G3kpBui\nSmiFmqJ6CbgFmAy8DMwDhgEdfg2ccqd55LAUz3AUy6117w577BE9sm3aFN3pPVfy8+KLsGJFCqhp\n2H7nnZse/dl++8JOfeW8IWr8vFiTHH0+wypkPIMmOO5+AYCZdXX3J+PF75rZwSGPIyJSrtLX6Rk8\nGMaNa7zOHR55BAYO3Dr5yXWvrz59mk5+dtkl/NTX7JkzGyU3ANfW13PZTTcVbYIjpStfp4k/AlwN\nvA3sBlzh7kcEP1AraYpKRMrZmjVNT33Nnx/dCT5tu+1g6NDcyc/QodEoU2tNq6lh2tNPb738kEOY\nlkq1+X1JeSv0WVTTgB8Do4A3gNPzdBwREWmhnj1hzz2jR7ZNm6Lr+uRKfp59Flat2rKtWTTCk1nr\nkz31lct7DHAAAAAgAElEQVTGbt1yLt/UlmxJpBm6Dk6Z0DxyWIpnOIplWPmIpzv8619Nn/X1z382\n3r6yMvfIz7/mzeKVa8/iuoxpqouHD+ewG28s2ikqfT7DKvnr4JjZUOD7wCrgUWCBu7+Qj2OJiEh+\nmUH//tFj//23Xv/xx7mv9vznP8P//R9s3JjechJ9u8ALn7qJHbqtxXp1p+rzZ7LSJ/G3v7V96ksk\nl3zV4NxBdMPNGuA64Hp3Pzv4gVqpnEdwREQ6wsaNTU991dfD6tWNt09PfWVOe6Wf77CDLngoWyt0\nDc7f3T1lZmPcfb2ZLcrTcUREpIh16RKNzAwdChMmNF6XOfWVOQL07ru5z/rq3XtLspOdAOleX5It\nXx+H0Wa2P9DNzEYDI/J0HGkhzSOHpXiGo1iGVUrxbG7qa82axvf6SidBr78Ov/1t7nt9NZUAVVS0\nrY+lFM9SULLXwclwPXAHMBqYCHwtT8cREZGE6tkTRo2KHtk2b44ueJg98lNfDw89BB9+2Hj7/v0b\nJz6ZCdBOO0GnToV5T1I4hboX1c7u/kHzW+a9H6rBEREpA8uXRwlPrgToH/+IEqS07t23TnzS/w4d\nGt1UVYpXoe5Ftbe7zzWzk4H0jg040t2/HOxAbaQER0RENmyA997LXftTXx+dFZbW1DV/0v8W+nYX\nsrVCJTg/dfdTzSwFpG+8acBB7t7h96Mq5wRH88hhKZ7hKJZhKZ7t4w5LlmxJdp58MsXmzTUNCdCi\nrFNm+vTJfcbX8OGw664qfM5WstfBcfdT46dT3f0vGQfPcd1MERGR4mIGO+4YPcaOjZKUzN/j7Ntd\npBOhv/wFfv3rxre76NIlutN7rgRo2DDo1avQ765lknLH99AjOINzLQa+6e4XBztQG5XzCI6IiORX\n+k7vuaa96uvho48abz9gQNPX/Nlpp46Z+sp5x/fhw5lYxFebLtQU1bvAezlWDXH3YcEO1EZKcERE\npKN89NGWhCf73wULGhc+9+jR9CnvVVX5K3y+dOJErpk9e6vll02cyNW/+11+DtpOhbrQ35nuPivH\nwYsz7SsjmpcPS/EMR7EMS/EMK2Q8+/aFffeNHtnWr99S+JydAD3xRDQ1lmYGgwY1nQA1dbPTluiS\neXGhDJ3Xrm37TjOU7HVw0smNmW0HfB3YE3id6Jo4IiIiksN228HIkdEjmzssXpx76mvWrKZvdpor\nAdp1V+jcuel+JOmO7/m6F9U9RDfafAfYDfiUu58Y/ECtpCkqERFJmvTNTnMlQPPnNy587to1muLK\nlQANGwZ/Tm1dg1Psd3wvSA1OxsG+7+7nZbT/RzfbFBERKaxNm2Dhwqav+bN8eePtd9wRBvedxcAV\nNzF0wFoqdurOoWeeWbTJDRT+ZpsNITMzA96Pnx/u7o/l6ZiyDZqXD0vxDEexDEvxDKvU49m5c3SP\nriFDYPz4rdcvW7Z1wXN9/STmrJnEsefDCSeE7U/J1uBkmGxmXwfmA0OAZWZ2BDAYGN7Ui8xsAnA0\nsARwd78qxzaTgWuBszILms1sPjAvbi5095PCvBUREZFk2n776LHffh3dk/DyNUX1A+BGomvgQHTb\nBgO+5e4XNPGansBcYJS7bzCzh4Fb3P2pjG2qgCrgCuAGd380Y90V7n5lM/3SFJWIiEiCNDVFlZf7\np7r7ue7+nrvPd/f5QP/4ec7kJjYWeM/d0+VQzwONJv3ifaSaeP1BZna+mV1lZmPb+x5ERESkdOVl\nisrM9gVOBiriRaOB5gbABhCdeZW2Ml7WUhe5+8tm1gN4xcyOdPf67I2mTJlCVVUVAJWVlVRXVzfM\nB6ZSKYBEttPPi6U/pd5WPMO108uKpT+l3k4vK5b+lHo7vaxY+lPq7fSy9uwvlUpRW1sL0PB7nku+\npqheAK5nS7Hxie7+9WZeMx642N0nxO1zgZ3d/Ts5tq0ja4oqa/0DwGPufnfW8rKdokqVeKFcsVE8\nw1Esw1I8w1I8w8pHPAt9mvhMd5+a0R7p7m8385p0Dc6e7r4+rsG5GZgDbHT3VRnb1gEzMi4sOB7o\n6u6Px+2XiIqQX8g6RtkmOCIiIklU6NPE68zsbqIL/RlwEDBhWy9w9zVmdgYw08yWAnPdvc7MpgPL\ngOkAZnYp0ZlZx5rZBnefTXTW1TQz+wywM/CL7ORGREREykc+p6geJJqiMuBIdz8m+IFaqZxHcDTM\nGpbiGY5iGZbiGZbiGVYhp6jyNYIzx91vzDj483k6joiIiMhW8jWCcxPRtFI9W0Zwvhz8QK1UziM4\nIiIiSVTQ6+AAnwM2A0PjRztu3i4iIiLSOvlKcL7l7lfGj2nA6Xk6jrRQ5jUIpP0Uz3AUy7AUz7AU\nz7AKGc+gCU58Y02AD8xscPwYApwS8jgiIiIi2xK0BsfMXnL3z5rZPKIbbaYNcfdhwQ7URqrBERER\nSZaCnEXl7p+Nn57l7o9kHHxSEy8RERERCS4vNTiZyU3cnpWP40jLaR45LMUzHMUyLMUzLMUzrJKt\nwREREREpBnm5Dk6xUg2OiIhIshT6OjgiIiIiHUYJTpnQPHJYimc4imVYimdYimdYqsERERERaQfV\n4IiIiEjJUg2OiIiIlA0lOGVC88hhKZ7hKJZhKZ5hKZ5hqQZHREREpB1UgyMiIiIlSzU4IiIiUjaU\n4JQJzSOHpXiGo1iGpXiGpXiGpRocERERkXZQDY6IiIiULNXgiIiISNlQglMmNI8cluIZjmIZluIZ\nluIZlmpwRERERNpBNTgiIiJSslSDIyIiImVDCU6Z0DxyWIpnOIplWIpnWIpnWKrBEREREWkH1eCI\niIhIyVINjoiIiJQNJThlQvPIYSme4SiWYSmeYSmeYZVtDY6ZTTCzm83sCjO7vIltJpvZO2Y2Kce6\nAWb2vpn9v/z3VkRERIpV0dTgmFlPYC4wyt03mNnDwC3u/lTGNlVAFXAFcIO7P5qxrhNwGzAA+L27\n35zjGKrBERERSZBSqMEZC7zn7hvi9vNAo1Ead5/v7qkmXn8BcAfwEaAsRkREpIwVU4IzAFiV0V4Z\nL2uWmY0H1rj7nwCLH5JB88hhKZ7hKJZhKZ5hKZ5hFTKeXQp2pOYtBioy2n3iZS3xReCfZnYhsBdQ\naWYfu3tt9oZTpkyhqqoKgMrKSqqrq6mpqQG2BF5ttdUuXDutWPpT6u20YulPqbfTiqU/pd5Oa8/+\nUqkUtbW1AA2/57kUYw3Onu6+Pq7BuRmYA2x091UZ29YBM9x9Vo793AW8rBocERGR5GuqBqdoRnDc\nfY2ZnQHMNLOlwFx3rzOz6cAyYDqAmV0KDAGONbMN7j47vQ8zOxX4NLC9mdW7++8K/05ERESkoxXN\nCE4hlPMITiqVahjqk/ZTPMNRLMNSPMNSPMPKRzxL4SwqERERkSA0giMiIiIlSyM4IiIiUjaU4JSJ\n7FP0pH0Uz3AUy7AUz7AUz7AKGU8lOCIiIpI4qsERERGRkqUaHBERESkbSnDKhOaRw1I8w1Esw1I8\nw1I8w1INjgQ3Z86cju5Coiie4SiWYSmeYSmeYRUynkpwysTy5cs7uguJoniGo1iGpXiGpXiGVch4\nKsERERGRxFGCUybmz5/f0V1IFMUzHMUyLMUzLMUzrELGs+xOE+/oPoiIiEhYuU4TL6sER0RERMqD\npqhEREQkcZTgiIiISOIowREREZHE6dLRHZC2MTMDfgv8AdgOGA6cCqwDTgOuAsa5+xvx9tsBtwPz\ngP7AfHefEa+rBr4VrxsAfMfdNxX0DXWwbcTzu8DHwGpgb+Bsd18cv+Z8oALoC8x299/Ey8s6nq2N\npZkdARwDvA58GviFuz8S76usYwlt+2zGr9sdeAmY7O6PxssUz7Z9148EqokGBcYBh7r7RsWzTd/3\nwv0WubseJfgADLg4o/0r4CtEX8K94w/IqIz1k4FfZbz2w/gDZMBfgQHxuhnAqR39/ooonldlLLsA\nmBk/HwPMip93Bt4iSnbKPp5tiOVpwC7x8wHA8oz9lHUs2xLPuN0D+CnwPHCE4tmuz+dQ4OcZ60bF\n+1A82xbPgv0WaYqqRHnkOgAz6wLsCvzd3ee4+9wcL/kn0C9+3odopGcNMAzo4e5L4nXPA5Py2vki\ntI14Xp6xWWdgVfz8SOCF+LWbgL8BNSierY6lu9/u7u9nLF8dPy/7WEKbPpsA1xCN4q7PWKZ40qZ4\nTgaWm9k5ZnYlsLNHv8CKJ22KZ8F+izRFVeLM7AvAOcBv3P2VprZz96fN7BUz+xnRh+tCd19tZqNp\n/B/GVUTZdFlqKp5mVgkcCvxXvKg/UVKTtpIobktRPIFWxTLTBcCZ8fMBKJYNWhpPM/sq8Jy7z49m\nD0hfH0TxzNCKz+cQYF/gm0S/mXPM7GhgBxTPBi2NZyF/izSCU+Lcfba7Hw4MM7MzmtrOzKYC27n7\nyUSjD982s/2BxURTK2m9gSU5dlEWcsXTzPoAPwJOcff0jVSWsHXcFjexvCzj2YpYEq87D5jr7r+M\nFymWGVoRzxrg38zsQmAwcEz8g6zveoZWxHMF8HI8UrEBmAuMRfFspKXxLORvkRKcEmVme8TFmWnz\nieaKG22W8XxXYBFEQ4pEH5xB7v4u8ImZ7RhvdyBRwVhZaSqeZtYPuBm4wN3fM7MvxetnEf1HDjPr\nCuwBPENU+1TW8WxDLDGzy4AF7l5rZjVmtj2gzyatj6e7n+ru0919OvAP4OE4aSz7zya06fP5JI3/\n2zoYeBPFE2hVPNMjYgX7LdKVjEuUmQ0DbgBeAboCuwNTiebcv000VHgP8IC7/zH+0NwEvAb0BLYH\nznD3TWa2N9G0wHtEZwR9x903F/gtdagm4nkW8CjR/PFH8aYr3f2o+DXfIYpXX+BRd/9tvLys49na\nWMb/R3cJ8Ea8fGeis1T+Ue6xhLZ9NuPXnUv034Jngdvc/UXFs83f9SvjbTsDH7r79fFyxbP13/eC\n/RYpwREREZHE0RSViIiIJI4SHBEREUkcJTgiIiKSOEpwREREJHGU4IiIiEjiKMERERGRxFGCIyIi\nIomjBEdEREQSRwmOiIiIJI4SHBEREUkcJTgiIiKSOF06ugOFZGa68ZaIiEjCuLtlLyu7ERx3L8vH\nFVdc0eF9SNJD8VQsi/WheCqexfzIRzybUnYJjoiIiCSfEpwyMX/+/I7uQqIonuEolmEpnmEpnmEV\nMp5KcMpEdXV1R3chURTPcBTLsBTPsBTPsAoZT9vW/FXSmJmX0/sVERFJOjPDVWQsIiIi5UAJTplI\npVId3YVEUTzDUSzDUjzDUjzDKmQ8leCIiIhI4qgGR0REREqWanBERESkbCjBKROaRw5L8QxHsQxL\n8QxL8QxLNTgiIiIi7aAaHBERESlZqsERERGRsqEEp0xoHjksxTMcxTIsxTOspMXTzPLyaKlCxrNL\nwY4kIiIiRSB0qUbLE5xCUg2OiIhImYhGW8InOB3526oaHBERESkbSnDKRNLmkTua4hmOYhmW4hmW\n4hmWroMjIiIi0g6qwRERESkT5VSDk7ezqMxsAnA0sARwd78qa313YAawEBgJfM/d347XnQhUA5uA\nene/PV6+HXAWcCXQz93XxMs7AdcBK4Eq4E53/2O+3puIiIgUt7xMUZlZT+BW4Gx3vxL4tJmNz9rs\nbGC+u38P+CFwZ/zaXYHz3P077n4h8HUzGxG/Zn/gYaB71r6OBSrc/TrgQuBua82J+WVA88hhKZ7h\nKJZhKZ5hKZ5hJaEGZyzwnrtviNvPA5OytjkCeBHA3V8D9jazCmAi8OeM7V4EDo+3e8bd5+U43hHA\nC/E2HwFrgb3CvBUREREpNfmaohoArMpor4yXtWSb/i14bUuO1z/XhlOmTKGqqgqAyspKqqurqamp\nAbZklkls19TUFFV/Sr2teKqtttql2N4i3a4J0i7k+0mlUtTW1gI0/J7nkpci43g66mJ3nxC3zwV2\ndvfvZGzzTLzNc3F7BbAL0XTTAe7+9Xj5TOAtd/9Rxms3A70yanDuBma7+71xey5wQjwylNkvFRmL\niEjZKqci4055Ot4fgCFxUTDAAcAsM+sbT0MBzCKaysLMRgNz3H01MBvYN2Nf+wOP5ThG5pvJ3Nf2\nRDU6rwd6L4mwdfYu7aF4hqNYhqV4hqV4hlXIeOZlisrd15jZGcBMM1sKzHX3OjObDiwDpgM3AjPM\n7BJgBPC1+LULzWyGmf2Q6Cyqn7h7PYCZDQFOIko/zzezB9z9TeDnwD5mdjkwGDhJQzUiIiLlS9fB\nERERKROaohIREREpYUpwyoTmkcNSPMNRLMNSPMNSPMMqZDyV4IiIiEjiqAZHRESkTKgGR0RERKSE\nKcEpE5pHDkvxDEexDEvxDEvxDEs1OCIiIiLtoBocERGRMqEaHBEREZESpgSnTGgeOSzFMxzFMizF\nMyzFM6ySvxeViIiUp2gKJCyVFkhbqAZHRESCCV/j0bH1HUmjGhwRERGREqYEp0xoHjksxTMcxTIs\nxTMsxTMsXQdHREREpB1UgyMiIsGoBqe4qQZHREREpIQpwSkTmkcOS/EMR7EMS/EMS/EMSzU4IiIi\nIu2gGhwREQlGNTjFTTU4IiIiIiVMCU6Z0DxyWIpnOIplWIpnWIpnWKrBEREREWkH1eCIiEgwqsEp\nbqrBERERESlhSnDKhOaRw1I8w1Esw1I8w1I8w1INjoiIiEg75K0Gx8wmAEcDSwB396uy1ncHZgAL\ngZHA99z97XjdiUA1sAmod/fb4+VVwKXAO0AVcJ67f2xmfYEfA3OAEcCT7n5fjj6pBkdEJI9Ug1Pc\nyqkGJy8Jjpn1BOYCo9x9g5k9DNzi7k9lbHMRsNHdZ5jZXvH6g81sV+A37r5PvN2fgK+4+ztm9jvg\nUnd/2cy+DQxw98vN7EJgoLufbWaVwEJ375WjX0pwRETySAlOcSunBCdfU1RjgffcfUPcfh6YlLXN\nEcCLAO7+GrC3mVUAE4E/Z2z3InC4mXUFatz95Xj5Cxn7/CfQP34+AFgc8L0kguaRw1I8w1Esw1I8\nw1I8wypkPLvkab8DgFUZ7ZXxspZs07+J5TsAnzSxz/uAI83sZmB34IymOjZlyhSqqqoAqKyspLq6\nmpqaGmBL4NVWW+3CtdOKpT+l3k7r6ONDul3TzjYF7X+xxbP4/z5tO3573k8qlaK2thag4fc8l3xN\nUY0HLnb3CXH7XGBnd/9OxjbPxNs8F7dXALsAxwIHuPvX4+UzgbeA24DV7t49Xv4Z4Cfuvq+Z/QBY\n7O7T49qevwIT3P29rH5pikpEJI80RVXcNEXVfn8AhpjZdnH7AGCWmfWNp6EAZhFNZWFmo4E57r4a\nmA3sm7Gv/YHH3H0jUGdmn42XHwj8Nn6+K9E0Fe6+FljL1iNGIiIiUibykuC4+xqiaaKZZnY1MNfd\n64CLgG/Fm91IlARdApwLfC1+7UJghpn90MxmEI3S1MevOR04PX7NnsD0ePnlwBfN7JJ4xOc37v5S\nPt5bqdp6eFLaQ/EMR7EMS/EMS/EMq5DxzFcNDu7+BPBE1rILM56vBb7dxGvvI6qryV7+HnEilLX8\n78Ax7eyyiIiIJITuRSUiIsGoBqe4lVMNTotGcMzsP4AKomvbzHP3jwP3T0RERCSYZmtwzOx6oisS\nHwJ0Bb6b705JeJpHDkvxDEexDEvxDEvxDKuQ8WxJkfEKdz+F6JYJrwIf5LlPIiIiIu3SbA2Omf3Q\n3c8xswvj68x8393PK1D/glINjohIfqkGp7iVUw1OS0Zw3jKzN4CTzOwl4G/BeyciIiISULMJjrvf\nCnwJuBI40d3vyHuvJDjNI4eleIajWIaleIaleIZVVNfBMbNJgLv7Q2Z2pJmtdPdFBeibiEgj0fB6\neJoCEUmeltTg3A2c7+6LzWwX4CJ3P7MgvQtMNTgipS2J9QNJoxqc4pbE71B7anD+4u6LAdz9fWBh\n6M6JiIiIhNSSBGcPM9vPzCrNbD9gt3x3SsLTPHJYimc4imVYimdYimdYRVWDA8wAfgqMJrqS8Vb3\nghIREREpJq2+F5WZ9XP3f+WpP3mlGhyR0pbE+oGkUQ1OcUvid6jN96IyswrgUKAXYMCRwJeD91BE\nREQkkJbU4PwWGA8MjR/b57VHkheaRw5L8QxHsQxL8QxL8Qyr2Gpw3nX3b6cbZjY0j/0RERERabeW\nXAdnGvA88Fa86BR3n5bfbuWHanBESlsS6weSRjU4xS2J36GmanBakuAsAv6esWiIuw8L3L+CUIIj\nUtqS+B/npFGCU9yS+B1qz4X+Lnb3cekH8M3w3ZN80zxyWIpnOIplWIpnWIpnWEVVg+Pud5nZp4H+\nwJvAk3nvlYiIiEg7tGSK6nzgCGABcBdwuLtfUIC+BacpKpHSlsTh9aTRFFVxS+J3qD1TVBXx1NTr\n7l4HfBS8dyIiIiIBtSTB6ZzVrshHRyS/NI8cluIZjmIZluIZluIZVlHV4ACbzOxxoKeZ/TvwSp77\nJCIiItIuLboXlZlNBD4NzHH33+e9V3miGhyR0pbE+oGkUQ1OcUvid6g918H5B/Cf7l7yIzdKcERK\nWxL/45w0SnCKWxK/Q+0pMn48M7kxs+FBeyYFoXnksBTPcBTLsBTPsBTPsIqtBmeVmZ0O/C1unwh8\no7kXmdkE4GhgCeDuflXW+u7ADGAhMBL4nru/Ha87EagGNgH17n57vLwKuBR4B6gCznP3j+N1pwD9\ngN5Atbt/sQXvTURERBKoJVNUbwPPZSz6tLvv28xregJzgVHuvsHMHgZucfenMra5CNjo7jPMbK94\n/cFmtivwG3ffJ97uT8BX3P0dM/sdcKm7v2xm3wYGuPvlZnYQ8F/ufk78mr3c/bUc/dIUlUgJS+Lw\netJoiqq4JfE71J4pqnPd/ZT0AzijBa8ZC7zn7hvi9vPApKxtjgBeBIiTkb3NrAKYCPw5Y7sXgcPN\nrCtQ4+4vx8tfyNjnCcB6MzvbzK4FtmtBH0VERCShmk1w3P03ZvYfZnZCPNLyegv2OwBYldFeGS9r\nyTb9m1i+A/BJE/scAuzm7v8DXA/8wsz6tqCfZUPzyGEpnuEolmEpnmEpnmEVVQ2OmV1PlHRsAN4A\nvgtMbeZli2l8QcA+8bJMS4jqZdJ6x9ssAUZkvfYt4F9Aj6ztl8TPVwBzANx9RXwH9NHAM9kdmzJl\nClVVVQBUVlZSXV1NTU0NsCXwaqutduHaaa3dHtLtmna2W3f8Ym939PvJ6EH8b0072xS0/8UWz+L/\n+7Tt+O15P6lUitraWoCG3/NcWlKDc4m7X2tmF7r7dDO7yN2/18xr0jU4e7r7+rgG52aiJGSju68y\nswuBze5+g5mNBn7k7oc0UYNzvLvXm9ljwOXu/pKZnQn0c/crzOwbwGfd/TQz60RUhHywuy/M6pdq\ncKRJ0dx0ePrMhZPE+oGkUQ1OcUvid6ipGpyWnEXVL6vdv7kXuPsaMzsDmGlmS4G57l5nZtOBZcB0\n4EZghpldQjRi87X4tQvNbIaZ/ZDoLKqfuHt9vOvTgcvN7AvAIODceHktMNrMriCaypqWndyItEz4\nL76IiBReS0ZwzgDOBDYT1cD82N3vKEDfgivnEZxUKtUw1Ce5te7/bFJsGard5l71f5/NaM1nM4n/\n9xlaR3/XkzaC09HxDK2jv0P5iGerR3DM7PvAhe5+q5mlgL2Av7j7m0F7JiIiIhJYkyM4ZnaBu1+f\nXXNjZke5+68L1sOAynkER5rX0f9nI83T36j4JW0EJ2mS+B1qSw3OfmZ2JXCwmXXLWH4QUJIJjoiI\niJSHbV0HZyrR6dnLgfnx4724LSVm61MEpX1SHd2BxNBnMyzFMyzFM6xCxnNbIzi3AMcCz7j7gvRC\nM3si770SERERaYeW1OBcnnmjTDOb6u4zC9bDgFSDI9uSxLnppNHfqPipBqe4JfE71FQNzrYSnJ8R\n3bF7MNH0VNpgdx+ehz7mnRIc2ZYkfvGTRn+j4qcEp7gl8TvU6pttuvvJwFeAXwJTgFPix8N56qPk\nkeaRQ0t1dAcSQ5/NsBTPsBTPsIqlBgd3f58tVwsGwMxq89khERERkfba1hTVF+M7iV+Rteogd5+Q\n/66Fpykq2ZYkDt0mjf5GxU9TVMUtid+hVk9RAfvF/+6DThMXERGRErKtBOcpMzsE+AVbEpx5KMEp\nSZpHDi3V0R1IDH02w1I8w1I8wyqWGpwfAH8BdgW6A+8Cw9DtkUVERKTIbasG53Pu/pyZfcfdZ2Qs\nb3RdnFKiGhzZliTOTSeN/kbFTzU4xS2J36G2nCb+XPx0WNaqQSE7JiIiIhLatmpw0jaa2aNmdqOZ\nPQasy3enJDzNI4eW6ugOJIY+m2EpnmEpnmEVSw0OAO4+1cwmAaOA2e4+K//dEhEREWm7Jmtwkkg1\nOLItSZybThr9jYqfanCKWxK/Q225Do6IiIhISVKCUyY0jxxaqqM7kBj6bIaleIaleIZVyHgqwRER\nEZHEUQ2OSCyJc9NJo79R8VMNTnFL4ndINTgiIiJSNpTglAnNI4eW6ugOJIY+m2EpnmEpnmGpBkdE\nRESkHVSDIxJL4tx00uhvVPxUg1PckvgdUg2OiIiIlI28JThmNsHMbjazK8zs8hzru5vZj8zsIjO7\n08xGZqw70cxmmNl0MzstY3mVmd0Rv+Y2M/tU1j4PMbMNZjYqX++rVGkeObRUR3cgMfTZDEvxDEvx\nDKuo7kXVFmbWE7gVGOXuG8zsYTMb7+5PZWx2NjDf3WeY2V7AncDBZrYrcJ677xPv609m9pS7vwPc\nBlzq7i+b2beBC4HL4+0GAMcCC/PxnkRERKR05KUGx8w+D/y3u0+I2+cAu7r7eRnbPBNv83zcXgHs\nSpSkjHX3r8fLbwTSyc0qd+8eL98HuMPd9zWzTsCPgfOBV4FJ7v5Gjn6pBkealMS56aTR36j4qQan\nuCXxO1ToGpwBwKqM9sp4WUu26d/E8h2ATzKWr8rY50XA7e6+PG5v9UZFRESkfORligpYDFRktPvE\ny7nny6IAAArvSURBVDItAXpntHvH2ywBRmS99i3gX0CPrO2XmFk3YE9gk5mNj7c/1cxmZU2JATBl\nyhSqqqoAqKyspLq6mpqaGmDL3GAS25nznsXQn2JsR1JATcZzmminnze1nqxtOv79FWs7vaw120fS\n7Zp2tmlX/4utnV7WkcePpNs17WxT0P4XWzyL/+/T+uO3N56pVIra2lqAht/zXPI1RdUTmAvs6e7r\nzexh4GZgDrDR3VeZ2YXAZne/wcxGAz9y90PiGpzfZNbgAMe7e72ZPQZc7u4vmdmZQD93vyLr2PPQ\nFNVWUqlUwwdFcmvd0G2KLV/0be5Vw+vNaM1nM4nD66F19Hc9aVNUHR3P0Dr6O5SPeDY1RZW36+CY\n2QTgGGApsN7drzaz6cAyd59uZt2BGcAiohGba+NCYszsBGA/YBPwprv/JF4+hKio+F1gEHCuu6+J\n13Uhmqo6B7gH+LG7/y2rT2Wb4EjzOvqLL83T36j4JS3BSZokfocKnuAUIyU4si1J/OInjf5GxU8J\nTnFL4ndIF/orc1vPv0r7pDq6A4mhz2ZYimdYimdYhYynEhwRERFJHE1RicSSOHSbNPobFT9NURW3\nJH6HNEUlIiIiZUMJTpnQPHJoqY7uQGLosxmW4hmW4hmWanBERERE2kE1OCKxJM5NJ43+RsVPNTjF\nLYnfIdXgiIiISNlQglMmNI8cWqqjO5AY+myGpXiGpXiGpRocERERkXZQDY5ILIlz00mjv1HxUw1O\ncUvid0g1OCIiIlI2lOCUCc0jh5bq6A4khj6bYSmeYSmeYakGR0RERKQdVIMjEkvi3HTS6G9U/FSD\nU9yS+B1SDY6IiIiUDSU4ZULzyKGlOroDiaHPZliKZ1iKZ1iqwRERERFpB9XgiMSSODedNPobFT/V\n4BS3JH6HVIMjIiIiZUMJTpnQPHJoqY7uQGLosxmW4hmW4hmWanBERERE2kE1OCKxJM5NJ43+RsVP\nNTjFLYnfIdXgiIiISNlQglMm/n979xsjV1WHcfz7S//EkvQP0NakFlwrGsCUrvoCiS8cjGAoJAQh\naTRGazVoDZQCtU1Ea9oYpHQTlD+x1qg1viAxJRILNSExjGitCDbbWFHR6hYx2kW0dgvEQnl8cc90\nZ5dd2Jm9c2f33ueTNJ1z7r0zZ545d+7Ze+7d9Txy3urdbkBpuG/my3nmy3nmy9fgmJmZmU2Cr8Ex\nS8o4N102/oymPl+DM7WVcR8a7xqcmR1+0Q8B1wCDgCRtHbX8TUAf8CzwDuAOSX9Kyz4O9AKngMOS\ndqb6HuBLwJ+BHuBWSS9ExGrgYuAw8B7gHkn7O/n+zMzMbGrq2BRVRJwBfBNYL2kLcFFEfHDUauuB\nAUl3AHcB30nbLiUbuGyQtAn4TEScl7bZAexI2xwCNqX6JcBNkvqArwPf6tR7m448j5y3ercbUBru\nm/lynvlynvkqyzU4lwBHJL2cyvuAK0etsxLYDyDpELAiIuYCHwZ+07TefuCKiJgF1CQ9Ofo5Jd0u\n6WSqnwEM5fx+zMzMbJro5BTVYkYOMo6nuomss2ic+rOBl5rqh0Y/Z2QTjOuAWybR9o7Impavic57\n1mq13F+72mrdbkBpuG/my3nmy3nmq8g8OznAOQrMbSrPT3XNBoF5TeV5aZ1B4Lym+vnA08C/gDmj\n1h9sFNLg5k7ge5IeH6tRq1evpqenB4AFCxbQ29t7OvDGqbNOlTOPMnxwrKf/2y0H9Xq9sPaXvZyp\nk9/n0yhTSPurUh7WKNcmWabQ9pe9PKxRrk2yTKHtL3t5WKNcy6Vc5Pup1+vs2rUL4PTxfCwdu4sq\nXYNzEHiXpJMRsRu4D+gHXpE0FBGbgFclbY+I5cC9kj6QrsHZI+nd6bl+DXxU0uGI+AmwWdITEXEj\nsFDSVyJiBtl1PLslPRYR10p6YFSbunoXVTfvLqg3DYRsbK19PnUmdhbHd4C8kVb6ZhnvAMlbt/f1\nst1F1e0889btfagTeRZ+F5WkFyNiLXB3RDwHHJT0aERsA/4NbAO+AfRFxG1kZ2w+nbZ9NiL6IuIu\nsruovi3pcHrqzwGbI+Jy4ByGp6K2A1cDy9NU0DJgxADHzMzMqsG/B6fY16dMP9mUTbd/srE35s9o\n6vP33NRWxn3If4vKzMzMKsMDnIp47QVmNjn1bjegNNw38+U88+U881Vknh7gmJmZWen4GpxiXx/P\nTU9dZZybLht/RlOfv+emtjLuQ74Gx8zMzCrDA5yK8Dxy3urdbkBpuG/my3nmy3nmy9fgWO76+/u7\n3YSScZ55cd/Ml/PMl/PMV5F5eoBTEceOHet2E0rGeebFfTNfzjNfzjNfRebpAY6ZmZmVjgc4FTEw\nMNDtJpTMQLcbUBrum/lynvlynvkqMs/K3Sbe7TaYmZlZvsa6TbxSAxwzMzOrBk9RmZmZWel4gGNm\nZmal4wGOmZmZlc7MbjfA2hPZHxR5CPgVMBt4O7AG+B9wPbAVuFTSU2n92cBO4K/AImBAUl9a1gt8\nPi1bDGyQdKrQN9Rlr5Pn14AXgBPACmC9pKNpmy8Ac4EzgUck7Un1lc6z1SwjYiVwHfA74CLgAUk/\nTs9V6Syhvb6ZtjsfeAJYJWlvqnOe7e3rVwG9ZCcFLgUuk/SK82xrfy/uWCTJ/6bhPyCALzaVHwQ+\nRrYTrkgd5MKm5auAB5u2fT51oAB+CyxOy/qANd1+f1Moz61NdRuBu9Pji4GH0+MZwNNkg53K59lG\nltcDb0mPFwPHmp6n0lm2k2cqzwG+C+wDVjrPSfXPtwE/bFp2YXoO59lenoUdizxFNU0pcztARMwE\nlgJ/kNQv6eAYm/wTWJgezyc70/MisAyYI2kwLdsHXNnRxk9Br5Pn5qbVZgBD6fFVwC/TtqeA3wM1\nnGfLWUraKenvTfUn0uPKZwlt9U2Ar5KdxT3ZVOc8aSvPVcCxiLg5IrYAS5QdgZ0nbeVZ2LHIU1TT\nXERcDtwM7JF0YLz1JP0sIg5ExPfJOtcmSSciYjkjvxiHyEbTlTRenhGxALgM+EiqWkQ2qGk4Tpbb\nczhPoKUsm20EbkyPF+MsT5tonhHxCeAXkgay2QMavx/EeTZpoX++FXgv8FmyY2Z/RFwDnI3zPG2i\neRZ5LPIZnGlO0iOSrgCWRcTa8daLiHXAbEmfJDv7cENEvA84Sja10jAPGBzjKSphrDwjYj5wL/Ap\nSY0/pDLIa3M7Ok59JfNsIUvSsluBg5J+lKqcZZMW8qwB74yITcC5wHXpgOx9vUkLef4XeDKdqXgZ\nOAhcgvMcYaJ5Fnks8gBnmoqIC9LFmQ0DZHPFI1ZrerwU+AdkpxTJOs45kv4CvBQRb07rvZ/sgrFK\nGS/PiFgI3AdslHQkIq5Nyx8m+5IjImYBFwCPkV37VOk828iSiPgy8DdJuyKiFhFnAe6btJ6npDWS\ntknaBjwD7E6Dxsr3TWirf/6Ukd+t5wJ/xHkCLeXZOCNW2LHIv8l4moqIZcB24AAwCzgfWEc2534D\n2anCHwD3S3o8dZp7gEPAGcBZwFpJpyJiBdm0wBGyO4I2SHq14LfUVePkeROwl2z++D9p1eOSrk7b\nbCDL60xgr6SHUn2l82w1y/QT3W3AU6l+CdldKs9UPUtor2+m7W4h+y74ObBD0n7n2fa+viWtOwN4\nXtKdqd55tr6/F3Ys8gDHzMzMSsdTVGZmZlY6HuCYmZlZ6XiAY2ZmZqXjAY6ZmZmVjgc4ZmZmVjoe\n4JiZmVnpeIBjZmZmpfN/49Ya1LjT2PUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90e7f53d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ck3N5HqVE88thKI7hKJZhKI5hKI7Zvf322+y///506tRyd+KNN95o9bohFF0HBzgAeN3d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90e165d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'H93')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short-Term Index Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_short.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [0.000 -0.500 0.000] |   0.651 |   0.651\n",
      "  25 | [0.200 -0.500 0.000] |  40.649 |   0.651\n",
      "  50 | [0.400 -0.500 0.250] | 160.988 |   0.651\n",
      "  75 | [0.000 -0.525 0.000] |   0.651 |   0.646\n",
      " 100 | [0.011 -0.236 0.001] |   0.580 |   0.577\n",
      " 125 | [0.009 -0.532 0.000] |   0.558 |   0.558\n",
      " 150 | [0.008 -0.599 0.000] |   0.558 |   0.558\n",
      " 175 | [0.008 -0.600 0.001] |   0.558 |   0.558\n",
      " 200 | [0.008 -0.600 0.001] |   0.558 |   0.558\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.557747\n",
      "         Iterations: 118\n",
      "         Function evaluations: 220\n",
      "CPU times: user 14.3 s, sys: 41 ms, total: 14.3 s\n",
      "Wall time: 14.3 s\n"
     ]
    }
   ],
   "source": [
    "%time opt_jump = BCC_calibration_short()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.008, -0.600, 0.001])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_jump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_jump)\n",
    "rf = open('11_cal/BCC97_jump_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options['Model'] = BCC_jump_calculate_model_values(opt_jump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K998fzOyUlcHRRwczOwccAK1bp7t3IiLpE/s6OGZ2lrvf2tDtUdIAR1qShQsr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\n8z//GhzXCUcq9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90dcefed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_results(opt_jump)\n",
    "plt.savefig('../images/11_cal/BCC97_jump_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Index Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibration Procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_full.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [18.447 0.026 0.978 -0.821 0.035 0.008 -0.600 0.001] |   3.398 |   3.398\n",
      "  25 | [19.073 0.026 0.977 -0.871 0.034 0.008 -0.586 0.001] |   0.820 |   0.820\n",
      "  50 | [19.358 0.025 0.966 -0.883 0.036 0.008 -0.578 0.001] |   0.239 |   0.208\n",
      "  75 | [19.107 0.025 0.968 -0.895 0.035 0.008 -0.580 0.001] |   0.156 |   0.153\n",
      " 100 | [19.107 0.025 0.968 -0.891 0.035 0.008 -0.580 0.001] |   0.152 |   0.151\n",
      " 125 | [19.087 0.025 0.968 -0.903 0.035 0.008 -0.579 0.001] |   0.151 |   0.146\n",
      " 150 | [19.088 0.025 0.967 -0.917 0.035 0.008 -0.577 0.001] |   0.146 |   0.145\n",
      " 175 | [19.301 0.025 0.967 -0.920 0.035 0.008 -0.574 0.001] |   0.141 |   0.141\n",
      " 200 | [20.414 0.025 0.962 -0.964 0.036 0.008 -0.557 0.000] |   0.123 |   0.123\n",
      " 225 | [21.282 0.025 0.960 -0.981 0.036 0.008 -0.546 0.000] |   0.111 |   0.107\n",
      " 250 | [21.497 0.025 0.960 -0.983 0.036 0.008 -0.543 0.000] |   0.107 |   0.106\n",
      " 275 | [21.528 0.025 0.960 -0.985 0.036 0.008 -0.543 0.000] |   0.106 |   0.106\n",
      " 300 | [21.549 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 325 | [21.555 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 350 | [21.560 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 375 | [21.558 0.025 0.960 -0.985 0.036 0.008 -0.542 0.000] |   0.106 |   0.106\n",
      " 400 | [21.578 0.025 0.959 -0.985 0.036 0.008 -0.541 0.000] |   0.106 |   0.106\n",
      " 425 | [21.605 0.025 0.959 -0.985 0.036 0.008 -0.539 0.000] |   0.106 |   0.106\n",
      " 450 | [21.661 0.025 0.959 -0.984 0.036 0.008 -0.537 0.000] |   0.106 |   0.106\n",
      " 475 | [21.717 0.025 0.958 -0.983 0.036 0.008 -0.534 0.000] |   0.106 |   0.105\n",
      " 500 | [21.858 0.025 0.957 -0.983 0.036 0.008 -0.527 0.000] |   0.105 |   0.105\n",
      " 525 | [21.939 0.025 0.956 -0.989 0.036 0.008 -0.520 0.000] |   0.104 |   0.104\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 1min 41s, sys: 169 ms, total: 1min 41s\n",
      "Wall time: 1min 41s\n"
     ]
    }
   ],
   "source": [
    "%time opt_full = BCC_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.212, 0.025, 0.952, -0.999, 0.036, 0.008, -0.501, 0.000])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_full)\n",
    "rf = open('11_cal/BCC97_full_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Option Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options['Model'] = BCC_calculate_model_values(opt_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hC/7ZAwf8KnDLl/sBcIsXw89/7u/17Olr5pFj+HC/CYuISHNVnz7vz5vZ/0tS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\ncxGRLGYGr73mm9sf/k4xf3z9mYRnPt5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90df0c6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/BCC97_full_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_full.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%run 11_cal/plot_implied_volatilities.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000916384905836\n",
      "0.00934023531613\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print np.sum(options['model_iv'] - options['market_iv'])\n",
    "# total absolute error\n",
    "print np.sum(abs(options['model_iv'] - options['market_iv']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4w9O/P7RpE+zQGafvZliKZziKZVih45nJy8QfBi4GziRKWk5292HN2G4EcCSw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90df19b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l3xKcnwOPA28Bw4Hj3P3MrA4iAyU4Itvv448bT3lPT4KWLo1mh5J69mw5+amo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\nxwMcMzMzq53/B4GirgEV7IYmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90df19150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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az92fBg4G/tjR95dGzVpf7rDkRRibiWZvJgwaxGk/HsOhOVao1qyBd95hk4FP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\ncCRJUuf8D5Pa+zGSKpppAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90e149990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'BCC97')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implied Volatility Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000000\n",
      "         Iterations: 270\n",
      "         Function evaluations: 485\n"
     ]
    }
   ],
   "source": [
    "%run 11_cal/BCC97_calibration_iv.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 | [22.212 0.025 0.952 -0.999 0.036 0.008 -0.501 0.000] |   0.102 |   0.102\n",
      "  25 | [22.379 0.025 0.966 -0.997 0.036 0.008 -0.505 0.000] |   0.100 |   0.100\n",
      "  50 | [22.615 0.025 0.971 -0.992 0.036 0.008 -0.515 0.000] |   0.098 |   0.094\n",
      "  75 | [23.543 0.025 1.001 -0.980 0.036 0.008 -0.536 0.000] |   0.082 |   0.082\n",
      " 100 | [26.268 0.025 1.111 -0.934 0.038 0.008 -0.585 0.000] |   0.057 |   0.053\n",
      " 125 | [27.073 0.025 1.145 -0.918 0.038 0.009 -0.591 0.000] |   0.053 |   0.053\n",
      " 150 | [27.334 0.024 1.156 -0.914 0.039 0.009 -0.598 0.000] |   0.053 |   0.053\n",
      " 175 | [27.443 0.024 1.159 -0.913 0.039 0.009 -0.600 0.000] |   0.053 |   0.052\n",
      " 200 | [27.446 0.024 1.159 -0.913 0.039 0.009 -0.600 0.000] |   0.052 |   0.052\n",
      " 225 | [27.476 0.024 1.160 -0.913 0.039 0.009 -0.600 0.000] |   0.052 |   0.052\n",
      " 250 | [27.735 0.025 1.166 -0.915 0.039 0.008 -0.599 0.000] |   0.052 |   0.052\n",
      " 275 | [28.197 0.025 1.174 -0.920 0.039 0.008 -0.597 0.000] |   0.052 |   0.052\n",
      " 300 | [28.055 0.025 1.169 -0.923 0.039 0.008 -0.595 0.000] |   0.052 |   0.051\n",
      " 325 | [28.152 0.025 1.172 -0.924 0.039 0.008 -0.595 0.000] |   0.051 |   0.051\n",
      " 350 | [28.270 0.025 1.176 -0.924 0.039 0.008 -0.596 0.000] |   0.051 |   0.051\n",
      " 375 | [28.678 0.025 1.187 -0.926 0.039 0.007 -0.598 0.000] |   0.050 |   0.050\n",
      " 400 | [28.997 0.025 1.196 -0.929 0.039 0.007 -0.599 0.000] |   0.050 |   0.050\n",
      " 425 | [28.827 0.025 1.192 -0.929 0.039 0.007 -0.599 0.000] |   0.050 |   0.050\n",
      " 450 | [28.467 0.025 1.183 -0.928 0.039 0.007 -0.600 0.000] |   0.049 |   0.049\n",
      " 475 | [28.399 0.025 1.179 -0.936 0.039 0.007 -0.600 0.000] |   0.048 |   0.048\n",
      " 500 | [28.356 0.025 1.174 -0.950 0.039 0.007 -0.599 0.000] |   0.046 |   0.046\n",
      " 525 | [28.335 0.025 1.172 -0.952 0.039 0.007 -0.599 0.000] |   0.046 |   0.046\n",
      "Warning: Maximum number of function evaluations has been exceeded.\n",
      "CPU times: user 1min 46s, sys: 20 ms, total: 1min 46s\n",
      "Wall time: 1min 46s\n"
     ]
    }
   ],
   "source": [
    "%time opt_iv = BCC_iv_calibration_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.473, 0.025, 1.175, -0.953, 0.039, 0.007, -0.600, 0.000])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmpl = r'''\n",
    "\\begin{itemize}\n",
    "    \\item $\\kappa_v = %.3f$\n",
    "    \\item $\\theta_v = %.3f$\n",
    "    \\item $\\sigma_v = %.3f$\n",
    "    \\item $\\rho = %.3f$\n",
    "    \\item $v_0 = %.3f$\n",
    "    \\item $\\lambda = %.3f$\n",
    "    \\item $\\mu = %.3f$\n",
    "    \\item $\\delta = %.3f$\n",
    "\\end{itemize}\n",
    "'''\n",
    "results = tmpl % tuple(opt_iv)\n",
    "rf = open('11_cal/BCC97_iv_results.tex', 'w')\n",
    "rf.writelines(results)\n",
    "rf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Option Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options['Model'] = BCC_calculate_model_values(opt_iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FbtkyPwBu0SL45S/9vV69fM08cowY4TdhERFpqRrS5/0lM/v/klSeusqhPu8W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\nv/FMwjOfaDOFUy4MMWwYDBvmd1fr1w9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90dbf11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "for mat in set(options['Maturity']):\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Call',\n",
    "                                          style='b', lw=1.5,\n",
    "                                          legend=False, ax=ax)\n",
    "    options[options.Maturity == mat].plot(x='Strike', y='Model',\n",
    "                                          style='ro', legend=False,\n",
    "                                          ax=ax)\n",
    "plt.xlabel('strike')\n",
    "plt.ylabel('option values')\n",
    "plt.grid(True)\n",
    "plt.savefig('../images/11_cal/BCC97_iv_calibration_quotes.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = '11_cal/cal_results_full_iv.h5'\n",
    "h5 = pd.HDFStore(filename, 'w')\n",
    "h5['options'] = options\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrated Implied Volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options = calculate_implied_volatilities(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000733627325165\n",
      "0.0070137001147\n"
     ]
    }
   ],
   "source": [
    "# total net error\n",
    "print np.sum(options['model_iv'] - options['market_iv'])\n",
    "# total absolute error\n",
    "print np.sum(abs(options['model_iv'] - options['market_iv']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zpTV7Nnz1VVjXr1/NCM+QIeGp6YVEn814FMu4FM94MhHLnDyqIbmj8a+A0whJ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\nLiIiIvkvJzU4ZnYMcKS7j07aJwFl7n5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xwVD9+rLfuyPi6oh4bqnfFBEfiYjNEfFvEXFa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90dce0a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bFVKC8zPgYeANYChwnLufmtgAclCCI7L9Nm+ObmSaa/bnww8b+ppFxc2ZZ3ul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\ngasi4nFgbWbeERGrgX8Bq4GvAWsi4iKaKzYfL8c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90df777d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HFsaMgXXr2hkxAvbbL0kvWpSkJ01qYfTo4vVrpPTs2bMb/vvW12l/n8uTzi3X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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90ddd3d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_implied_volatilities(options, 'BCC97_iv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://python-for-finance.com\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
